Description Usage Arguments Value References See Also Examples
Estimate how important individual features or groups of features are by contrasting prediction performances. For method “permutation.importance” compute the change in performance from permuting the values of a feature (or a group of features) and compare that to the predictions made on the unmcuted data.
1 2 3 4 | generateFeatureImportanceData(task, method = "permutation.importance",
learner, features = getTaskFeatureNames(task), interaction = FALSE,
measure, contrast = function(x, y) x - y, aggregation = mean, nmc = 50L,
replace = TRUE, local = FALSE)
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task |
[ |
method |
[ |
learner |
[ |
features |
[ |
interaction |
[ |
measure |
[ |
contrast |
[ |
aggregation |
[ |
nmc |
[ |
replace |
[ |
local |
[ |
[FeatureImportance
]. A named list which contains the computed feature importance and the input arguments.
Object members:
res |
[ |
interaction |
[ |
measure |
[ |
The measure used to compute performance.
contrast |
[ |
aggregation |
[ |
replace |
[ |
nmc |
[ |
local |
[ |
Jerome Friedman; Greedy Function Approximation: A Gradient Boosting Machine, Annals of Statistics, Vol. 29, No. 5 (Oct., 2001), pp. 1189-1232.
Other generate_plot_data: generateCalibrationData
,
generateCritDifferencesData
,
generateFilterValuesData
,
generateFunctionalANOVAData
,
generateLearningCurveData
,
generatePartialDependenceData
,
generateThreshVsPerfData
,
getFilterValues
,
plotFilterValues
1 2 3 4 | lrn = makeLearner("classif.rpart", predict.type = "prob")
fit = train(lrn, iris.task)
imp = generateFeatureImportanceData(iris.task, "permutation.importance",
lrn, "Petal.Width", nmc = 10L, local = TRUE)
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